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 A new bioinformatics tool to recover missing gene expression in single-cell RNA sequencing data
Jingyi Jessica Li*
Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA
*Correspondence to:Jingyi Jessica Li , Email:jli@stat.ucla.edu
J Mol Cell Biol, Volume 13, Issue 1, January 2021, Pages 1-2  https://doi.org/10.1093/jmcb/mjaa053

Single-cell RNA sequencing (scRNA-seq) is a burgeoning field where experimental techniques and computational methods have been under rapid evolution in the past 6 years. These technological advances have allowed biomedical researchers to identify new cell types, delineate cell sub-populations, and infer cell differentiation trajectories in various tissue samples. Among the important features extractable from scRNA-seq data, the predominant ones are individual genes’ expression levels in single cells. Most analyses require a preprocessing step that converts a scRNA-seq dataset into a count matrix, where rows correspond to cells (or genes), columns correspond to genes (or cells), and entries are counts, i.e. a count is the number of sequenced reads or uniquely mapped identifiers (UMIs) mapped to a gene in a cell. Single-cell count matrices are highly sparse; for example, a typical matrix constructed from a droplet-based dataset may have >90% of counts as zeros.